Large-scale Underwater Networks Research Articles

Localization Algorithms in Large-Scale Underwater Acoustic Sensor Networks: A Quantitative Comparison

Recently underwater acoustic sensor networks (UASNs) have drawn much attention because of their great value in many underwater applications where human operation is hard to carry out. In this paper, we introduce and compare the performance of four localization algorithms in UASNs, namely, distance vector-hop (DV-hop), a new localization algorithm for underwater acoustic sensor networks (NLA), large-scale hierarchical localization (LSHL), and localization scheme for large scale underwater networks (LSLS). The four algorithms are all suitable for large-scale UASNs. We compare the localization algorithms in terms of localization coverage, localization error, and average energy consumption. Besides, we analyze the impacts of the ranging error and the number of anchor nodes on the performance of the localization algorithms. Simulations show that LSHL and LSLS perform much better than DV-hop and NLA in localization coverage, localization error, and average energy consumption. The performance of NLA is similar to that of the DV-hop. The advantage of DV-hop and NLA is that the localization results do not rely on the number of anchor nodes; that is, only a small number of anchor nodes are needed for localization.

Super-Resolution Information Collection in Underwater Sensor Networks with Random Node Deployment: A Compressed Sensing Approach

In order to accurately realize the required information of the vast ocean area in a practical way, in this paper a novel compressed sensing (CS) based information collection scheme is proposed for the large-scale underwater networks. The proposed scheme consists of two main components: (1) A random node deployment method along with the consequent data gathering and transmitting; (2) A super-resolution information reconstruction method. The former is aimed at reducing the required sensor nodes to an affordable number, and the latter is aimed at enhancing the spatial resolution of information map. Compared to the traditional data collection methods, in the proposed scheme much fewer sensors, as well as less energy consumption and less channel bandwidth, are required to obtain the accurate information map of the same resolution. Mathematical analysis and experiment results both illustrate the validity and efficiency of the proposed scheme, not only for the synthesized scenario but also for the real scenario. Furthermore, because most of the nature phenomena are sparse in certain domain (for example, spatial domain or frequency domain), the CS-based scheme can be widely used for the large-scale ocean monitoring purposes, as well as other appreciate situations.